Simulating Carbon and Water Balances in the Southern Boreal Forest. Omer Yetemen, Alan Barr, Andrew Ireson, Andy Black, Joe Melton

Transcription

1 Simulating Carbon and Water Balances in the Southern Boreal Forest Omer Yetemen, Alan Barr, Andrew Ireson, Andy Black, Joe Melton

2 Research Questions: How will climate change (changes in temperature and moisture) alter the vegetation dynamics of the southern boreal forest? What will be the role of hydrology in mediating change?

3 Ecozone-level: Vegetation Distribution Long-term mean CMI The ecotone in western Canada is: Coincident with the P-PET isolines (CMI) Makes it sensitive to climate change. CMI = P-PET Climate Moisture Index (cm) Precipitation Pot. Evapotranspiration Hogg, 1994 Hogg, E.H., Climate and the southern limit of the western Canadian boreal forest.

4 Strategy Goal: to understand and simulate vegetation response to climate change Coupled carbon and water cycles; importance of water stress To assess a model s appropriateness, evaluate its performance against multiple constraints from fluxtower observations, including a) C fluxes (NEP=GPP-RE) and C stock accrual (dead and living) b) Stand-level water balance (ET, soil moisture, R=P-ET-ΔS) c) Mean state and inter-annual variability

5 Strategy: Road Map Evaluate CLASS-CTEM at flux towers across three plant functional types (PFT): ENF (OJP), DBF (OA), GRL (AB-GRL) Use local soil and vegetation information where known Evaluate each PFT individually using local forcing data Run all three PFTs at each location with competition turned on Run at larger scale, perhaps N-S transect

6 Ecosystem Carbon Balance Photosynthesis (GPP) Ecosystem Respiration (RE) Flux towers measure net ecosystem exchange (NEP), the net uptake of carbon by an ecosystem. NEP results as the balance of carbon uptake by gross ecosystem photosynthesis (GEP) and carbon release by ecosystem respiration (RE) NEP = GEP - RE

7 Functional Differences between CLASS and CLASS-CTEM: Parameterization of canopy conductance CLASS: Jarvis-Stewart g c =g cmax *f(sw )*f(ta)*f(vpd)*f(vwc) CLASS-CTEM: derived from photosynthetic rate in CTEM and passed to CLASS

8 Vegetation Controls on Canopy Fluxes in CLASS-CTEM CLASS solar radiation air temperature & humidity precipitation soil water content photosynthesis (GPP) [CO 2 ] transpiration wind speed longwave radiation canopy conductance LAI (dynamic) CTEM

9 Functional Differences between CLASS and CLASS-CTEM Prescribed (CLASS) vs. dynamic (CTEM) vegetation Fixed versus dynamic LAI and root depth Static versus dynamic spatial distribution of plant functional types (CTEM options for competition / migration and disturbance)

10 BERMS Old Jack Pine Site Precipitation: 468 mm LAI: 2-3 Biomass: 6.7 kgc/m 2

11 Annual Precipitation: OJP Site Severe drought in followed by wet years.

12 Base-line model performance: - 14-yr observation period from 1997 to yr spin up, burn the site (destruction of organic carbon), run with historic run from , then observed climatology ( ).

13 Base-line model performance: LAI Roughly similar runoff productions: 30.9% and 26.5% with different rooting scheme and photosynthesis parameter* * Vc max, the maximum rate of Rubisco carboxylase activity

14 Comparing Annual Fluxes CLASS-CTEM versus Flux Tower ET NEP=GPP-RE

15 CTEM Photosynthesis (GPP) in relation to Water Stress

16 Variation in NEP with stand age post-fire at OJP: comparing CTEM with flux-tower observations HJP02 HJP94 HJP75 OJP CTEM

17 Climate sensitivity simulations: - Each individual year assumed to represent one climate scenario yr spin up

18 Effect of Annual Precipitation on Simulated LAI (Leaf Area Index)

19 Take-home points CTEM is well suited to studies of vegetation dynamics CTEM represents the mean response well but overestimates inter-annual variability, related excess water stress In resolving this issue we face the problem of equifinality; water stress is affected by a host of soil and vegetation properties

20 Take-home points There is a trade off between soil and plant parameters The C cycle responds much more sensitively than the water cycle to changes in climate or model parameters. We are optimistic about the appropriateness of this model for hydrologically-mediated vegetation dynamics in the western boreal forest.

21 Little tweaks θ r must be revised for sandy soils Soil-texture transfer functions may be limiting

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